import datetime
import os

import tensorflow as tf
from keras.callbacks import TensorBoard
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np

# GPU配置示例
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        # 设置GPU内存增长
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        print(f"检测到 {len(gpus)} 个GPU设备，已配置内存增长")
    except RuntimeError as e:
        print(f"GPU配置错误: {e}")


# TensorBoard回调创建示例
def create_tensorboard_callback(log_dir_root="logs/fit"):
    # 创建基于时间戳的唯一日志目录
    timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
    log_dir = os.path.join(log_dir_root, timestamp)

    # 确保目录存在
    os.makedirs(os.path.dirname(log_dir), exist_ok=True)
    os.makedirs(log_dir, exist_ok=True)

    return TensorBoard(log_dir=log_dir, histogram_freq=1)

class RCNN:
    """
    基于Keras的RCNN实现
    """
    
    def __init__(self, num_classes, input_shape=(224, 224, 3)):
        """
        初始化RCNN模型
        
        Args:
            num_classes: 分类数量（包括背景类）
            input_shape: 输入图像的形状
        """
        self.num_classes = num_classes
        self.input_shape = input_shape
        self.model = None
        
    def build_cnn_base(self):
        """
        构建用于特征提取的CNN基础网络
        这里使用简化版的VGG-style网络作为特征提取器
        """
        model = keras.Sequential([
            # 第一个卷积块
            layers.Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=self.input_shape),
            layers.Conv2D(64, (3, 3), activation='relu', padding='same'),
            layers.MaxPooling2D((2, 2), strides=(2, 2)),
            
            # 第二个卷积块
            layers.Conv2D(128, (3, 3), activation='relu', padding='same'),
            layers.Conv2D(128, (3, 3), activation='relu', padding='same'),
            layers.MaxPooling2D((2, 2), strides=(2, 2)),
            
            # 第三个卷积块
            layers.Conv2D(256, (3, 3), activation='relu', padding='same'),
            layers.Conv2D(256, (3, 3), activation='relu', padding='same'),
            layers.Conv2D(256, (3, 3), activation='relu', padding='same'),
            layers.MaxPooling2D((2, 2), strides=(2, 2)),
            
            # 第四个卷积块
            layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
            layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
            layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
            layers.MaxPooling2D((2, 2), strides=(2, 2)),
            
            # 第五个卷积块
            layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
            layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
            layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
        ])
        
        return model
    
    def roi_pooling_layer(self, pool_size=(7, 7)):
        """
        实现ROI池化层
        注意：这是一个简化实现，在实际应用中可能需要更复杂的实现
        """
        def roi_pooling(inputs):
            feature_map, rois = inputs
            # 这里我们简化处理，实际的ROI池化需要更复杂的实现
            # 返回特征图的全局平均池化结果
            return tf.reduce_mean(feature_map, axis=[1, 2], keepdims=True)
        
        return layers.Lambda(roi_pooling)
    
    def build_rcnn_model(self):
        """
        构建完整的RCNN模型
        """
        # 输入层
        input_image = layers.Input(shape=self.input_shape, name='input_image')
        input_rois = layers.Input(shape=(None, 4), name='input_rois')  # ROI坐标 [x, y, w, h]
        
        # CNN特征提取器
        cnn_base = self.build_cnn_base()
        feature_maps = cnn_base(input_image)
        
        # ROI池化层（简化版）
        # 在实际实现中，这里应该是一个真正的ROI池化层
        # 我们使用全局平均池化作为简化
        roi_pooled = layers.GlobalAveragePooling2D()(feature_maps)
        
        # 全连接层
        fc1 = layers.Dense(4096, activation='relu', name='fc1')(roi_pooled)
        fc1 = layers.Dropout(0.5)(fc1)
        
        fc2 = layers.Dense(4096, activation='relu', name='fc2')(fc1)
        fc2 = layers.Dropout(0.5)(fc2)
        
        # 分类分支
        cls_scores = layers.Dense(self.num_classes, activation='softmax', name='cls_scores')(fc2)
        
        # 边框回归分支
        bbox_pred = layers.Dense(self.num_classes * 4, activation='linear', name='bbox_pred')(fc2)
        
        # 构建模型
        self.model = keras.Model(inputs=[input_image, input_rois], 
                                outputs=[cls_scores, bbox_pred], 
                                name='RCNN')
        
        return self.model
    
    def compile_model(self, learning_rate=0.001):
        """
        编译模型
        """
        if self.model is None:
            self.build_rcnn_model()
            
        optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
        self.model.compile(
            optimizer=optimizer,
            loss={
                'cls_scores': 'categorical_crossentropy',
                'bbox_pred': 'mean_squared_error'
            },
            loss_weights={
                'cls_scores': 1.0,
                'bbox_pred': 1.0
            },
            metrics={
                'cls_scores': 'accuracy'
            }
        )
        
        return self.model

def create_simple_rcnn(num_classes, input_shape=(224, 224, 3)):
    """
    创建一个简化版的RCNN模型用于演示
    
    Args:
        num_classes: 类别数量（包括背景）
        input_shape: 输入图像形状
    
    Returns:
        编译好的RCNN模型
    """
    # 输入
    input_image = layers.Input(shape=input_shape)
    
    # 特征提取网络 (简化版)
    x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(input_image)
    x = layers.MaxPooling2D((2, 2))(x)
    x = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(x)
    x = layers.MaxPooling2D((2, 2))(x)
    x = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(x)
    x = layers.MaxPooling2D((2, 2))(x)
    
    # 全局平均池化替代ROI池化
    x = layers.GlobalAveragePooling2D()(x)
    
    # 全连接层
    x = layers.Dense(512, activation='relu')(x)
    x = layers.Dropout(0.5)(x)
    
    # 分类输出
    cls_output = layers.Dense(num_classes, activation='softmax', name='classification')(x)
    
    # 回归输出
    bbox_output = layers.Dense(4, activation='linear', name='bbox_regression')(x)
    
    # 创建模型
    model = keras.Model(inputs=input_image, outputs=[cls_output, bbox_output])
    
    # 编译模型
    model.compile(
        optimizer='adam',
        loss={
            'classification': 'categorical_crossentropy',
            'bbox_regression': 'mse'
        },
        metrics={
            'classification': 'accuracy'
        }
    )
    
    return model

def example_usage():
    """
    RCNN使用示例
    """
    print("RCNN网络实现示例")
    print("=" * 50)
    
    # 创建RCNN实例
    rcnn = RCNN(num_classes=21)  # 20个物体类别 + 1个背景类别
    
    # 构建模型
    model = rcnn.build_rcnn_model()
    
    # 编译模型
    model = rcnn.compile_model()
    
    # 打印模型结构
    model.summary()
    
    # 创建简化版RCNN示例
    print("\n创建简化版RCNN模型:")
    simple_rcnn = create_simple_rcnn(num_classes=5)  # 包括背景类
    simple_rcnn.summary()

if __name__ == "__main__":
    example_usage()